He Fengjie, Zhu Ming, Fan Jiawei, Ma Edwin, Zhai Shengjie, Zhao Hui
Department of Mechanical Engineering, University of Nevada, Las Vegas, NV, 89154, USA.
Department of Electrical and Computer Engineering, Engineering, University of Nevada, Las Vegas, NV, 89154, USA.
Adv Sci (Weinh). 2023 Aug;10(24):e2302935. doi: 10.1002/advs.202302935. Epub 2023 Jun 26.
Treating potential polluted water sources is urgent and challenging, especially for natural water sources. Numerous research groups focus on either smart water monitoring or new adsorbent. However, either aspect alone is insufficient for complex nature water source treatment. Here, integrating the state-of-art machine learning technique, a sustainable silk-based bioadsorbent, and wireless Internet of Things, an integrated automated drone-delivery solar driven onsite water monitoring & treatment system (WMTS) for the contaminated nature water sources is developed. In short, the embedded sensors and microprogrammed control unit capture and upload the real-time monitoring data to the cloud server for data analysis and optimized treatment strategy. Meanwhile, a grid map system based on the satellite remote sensing images directs the minimum number of WMTS units to cover the entire polluted region. Finally, unmanned aerial vehicles provide autonomous dispatch, operation, and maintenance, especially in hard-to-reach sites. Overall, this work offers a general, sustainable, energy-efficient, and closed-loop solution toward efficiently alerting and on-site treating nature water source contamination.
处理潜在的受污染水源既紧迫又具有挑战性，尤其是对于天然水源而言。众多研究团队要么专注于智能水监测，要么专注于新型吸附剂。然而，仅从这两个方面中的任何一个单独来看，都不足以应对复杂的天然水源处理。在此，通过整合最先进的机器学习技术、一种可持续的丝绸基生物吸附剂以及无线物联网，开发了一种用于受污染天然水源的集成自动化无人机投递太阳能驱动现场水监测与处理系统（WMTS）。简而言之，嵌入式传感器和微程序控制单元捕获实时监测数据并上传至云服务器进行数据分析和优化处理策略。同时，基于卫星遥感图像的网格地图系统指导使用最少数量的WMTS单元覆盖整个污染区域。最后，无人机提供自主调度、运行和维护，特别是在难以到达的地点。总体而言，这项工作为有效预警和现场处理天然水源污染提供了一个通用、可持续、节能且闭环的解决方案。
